菜单
Sets up a long-running Maestro cluster environment using Azure ARO-HCP infrastructure with both service and management clusters
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基于 SOC 职业分类
This skill should be used when tracing ManifestWork resources through the Maestro system to find relationships between user-created work names, resource IDs, and applied manifests, or to debug manifest application issues across the management cluster and database.
This skill should be used when tracing resource requests through the Maestro system to debug resource lifecycle issues, track request flow from client to agent and back, or troubleshoot failures in resource creation, update, or deletion operations across the server-agent-server pipeline. Supports both Kubernetes log files and ARO HCP Kusto CSV exports.
Diagnoses failed Maestro cluster deployments by analyzing Helm releases, pod status, and resource conflicts
Runs end-to-end or upgrade tests on existing long-running Maestro clusters deployed in Azure AKS
| name | setup-maestro-cluster |
| description | Sets up a long-running Maestro cluster environment using Azure ARO-HCP infrastructure with both service and management clusters |
| category | Infrastructure |
| tags | ["azure","aks","maestro","deployment","cluster","aro-hcp"] |
Sets up a long-running Maestro cluster environment using Azure ARO-HCP infrastructure. This will deploy both service and management clusters.
Prerequisites:
Environment variables set:
USER=oasis (if not already set)PERSIST=trueGITHUB_ACTIONS=trueGOTOOLCHAIN=go1.24.4Note: This deployment typically takes 25-30 minutes to complete.
#!/bin/bash
# Execute the cluster setup script
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
exec "$SCRIPT_DIR/scripts/setup.sh" "$@"